Web Page Requests Research Articles

Exploiting the untapped functional potential of Memento aggregators beyond aggregation

AbstractWeb archives capture, retain, and present historical versions of web pages. Viewing web archives often amounts to a user visiting the Wayback Machine homepage, typing in a URL, then choosing a date and time significant of the capture. Other web archives also capture the web and use Memento as an interoperable point of querying their captures. Memento aggregators are web accessible software packages that allow clients to send requests for past web pages to a single endpoint source that then relays that request to a set of web archives. Though few deployed aggregator instances exist that exhibit this aggregation trait, they all, for the most part, align to a model of serving a request for a URI of an original resource (URI-R) to a client by first querying then aggregating the results of the responses from a collection of web archives. This single tier querying need not be the logical flow of an aggregator, so long as a user can still utilize the aggregator from a single URL. In this paper, we discuss theoretical aggregation models of web archives. We first describe the status quo as the conventional behavior exhibited by an aggregator. We then build on prior work to describe a multi-tiered, structured querying model that may be exhibited by an aggregator. We highlight some potential issues and high-level optimization to ensure efficient aggregation while also extending on the state-of-the-art of memento aggregation. Part of our contribution is the extension of an open-source, user-deployable Memento aggregator to exhibit the capability described in this paper. We also extend a browser extension that typically consults an aggregator to have the ability to aggregate itself rather than needing to consult an external service. A purely client-side, browser-based Memento aggregator is novel to this work.

Mobile-First Usability Guideline for Responsive E-Commerce Websites

Handheld devices such as smartphones, tablets, and PDAs are commonly used for online shopping. In order to satisfy web page requests on these devices, web developers are using a responsive web design approach, where websites dynamically adapt to different screen sizes and orientations. One of the challenges faced by web developers is to provide a usable experience across different devices. Motivated by this, a mobile-first usability guideline was established by extracting good usability practices from five popular responsive e-commerce websites. Validation of the usability guideline was conducted, and results indicate that it will have a positive impact on the overall user experience.

Research on ASP. NET AJAX Framework and Its Application in Web Development

Web page requests is the common way for applications to implement human-computer interaction, but full page refreshing of the page reduces the effects of users’experience, increases the pressure of servers and slows down the speed of program response. Partial update pages with AJAX asynchronous transfer technology can increase the efficiency of program interactions and give user a friendly program interface. Microsoft’ s ASP. NET AJAX framework can greatly reduce the difficulty of programmers in developing AJAX applications. This paper introduces AJAX engine principle and ASP. NET AJAX framework firstly, and then describes ASP. NET AJAX configuration in the Web site, at the end we imported AJAX application in the design of online examination system in order to achieve asynchronous partial update , therefore a friendly user interactive interface is brought about.

Mining groups of mobile users

This paper presents a new approach in data analysis for monitoring of mobile devices. Our model uses passive monitoring of mobile devices based on ideas of network proximity. This monitoring uses network protocol analysis for Wi-Fi and Bluetooth and lets us collect information on mobile visitors. Collected information is a direct analogue for weblog and website usage data, but operates with real visitors real mobile devices, rather than with abstract requests for web pages. We describe a new approach for processing of these data, which can detect some forms of relationships between mobile users. Our algorithm uses time-based clustering rather than traditional K-means approach. A traditional clustering algorithm K-means might find clusters and cluster centres for the given K. For our time stamped events we are not concerned with finding cluster centres. Our algorithm should only assign collected points to clusters as long as the segmentation remains the same.

Client-Side Detection of Clickjacking Attacks

Clickjacking attacks are emerging threat for web application users where click operations performed by victims lead to security breaches such as compromising webcams and posting unintended messages. Effective client-side defense technique could prevent the possible victims. This paper presents a client side approach to detect clickjacking attacks. The authors' approach examines web page requests and responses; the proposed approach is designed to detect advanced attack types such as cursorjacking, double click, and history object-based attacks. They evaluate the proposed approach with a set of legitimate and malicious websites. The results indicate that our approach has low false positive and false negative rates. The overhead imposed by the proposed approach is negligible.

Integrating Vague Association Mining with Markov Model

The increasing demand of World Wide Web raises the need othe user’s web page request. The most widely used approach to predict the web pages is the pattern discovery process of Web usmining. This process involves inevitability of many techniques like Markov model, association rules and cluster ing. Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov models. This paper is introducing the vague Rules withMarkov models for more accur acy using the vague set theory.

Sequence-order-independent network profiling for detecting application layer DDoS attacks

Distributed denial of service (DDoS) attacks, which are a major threat on the Internet, have recently become more sophisticated as a result of their ability to exploit application-layer vulnerabilities. Most defense methods are designed for detecting DDoS attacks on IP and TCP layers and consequently have difficulty in detecting this new type of DDoS attack. With the profiling of web browsing behavior, the sequence order of web page requests can be used for detecting the application-layer DDoS (App-DDoS) attacks. However, the sequence order may be more harmful than helpful in the profiling of web browsing behaviors because it varies significantly for different individuals and different browsing behaviors. This article introduces a sequence-order-independent method for the profiling of network traffic and the detection of a new type of App-DDoS attacks. Four attributes are extracted from web page request sequences without consideration of the sequence order of requested pages. A model based on the multiple principal component analysis is proposed for the profiling of normal web browsing behaviors, and its reconstruction error is used as a criterion for detecting DDoS attacks. The proposed method is experimentally confirmed with various types of new App-DDoS attacks.

A Query based Approach to Reduce the Web Crawler Traffic using HTTP Get Request and Dynamic Web Page

The functions of Web crawler download information from web for search engine. Web pages changed without any notice. Web crawler has to revisit web site to download updated and new web pages. It is estimated 40% of current web traffic is generated by web crawler. This paper proposes query based approach to inform updates on web site to web crawler using Dynamic web page and HTTP GET Request. Dynamic web page generates HTML based response having list of updates on web site after crawler last visit. Web crawler only visits updated web pages instead of visiting full web sites for updates. Proposed scheme is tested & results show that it is very promising.

Concurrency in Web Access Patterns Mining

Web usage mining is an interesting application of data mining which provides insight into customer behaviour on the Internet. An important technique to discover user access and navigation trails is based on sequential patterns mining. One of the key challenges for web access patterns mining is tackling the problem of mining richly structured patterns. This paper proposes a novel model called Web Access Patterns Graph (WAP-Graph) to represent all of the access patterns from web mining graphically. WAP-Graph also motivates the search for new structural relation patterns, i.e. Concurrent Access Patterns (CAP), to identify and predict more complex web page requests. Corresponding CAP mining and modelling methods are proposed and shown to be effective in the search for and representation of concurrency between access patterns on the web. From experiments conducted on large-scale synthetic sequence data as well as real web access data, it is demonstrated that CAP mining provides a powerful method for structural knowledge discovery, which can be visualised through the CAP-Graph model.

Composable, scalable, and accurate weight summarization of unaggregated data sets

Many data sets occur as unaggregated data sets , where multiple data points are associated with each key. In the aggregate view of the data, the weight of a key is the sum of the weights of data points associated with the key. Examples are measurements of IP packet header streams, distributed data streams produced by events registered by sensor networks, and Web page or multimedia requests to context distribution servers. We aim to combine sampling and aggregation to provide accurate and efficient summaries of the aggregate view. However, data points are scattered in time or across multiple servers and hence aggregation is subject to resource constraints on the size of summaries that can be stored or transmitted. We develop a summarization framework for unaggregated data where summarization is a scalable and composable operator, and as such, can be tailored to meet resource constraints. Our summaries support unbiased estimates of the weight of subpopulations of keys specified using arbitrary selection predicates. While we prove that under such scenarios there is no variance optimal scheme, our estimators have the desirable properties that the variance is progressively closer to the minimum possible when applied to a "more" aggregated data set. An extensive evaluation using synthetic and real data sets shows that our summarization framework outperforms all existing schemes for this fundamental problem, even for the special and well-studied case of data streams.

Soil-Web: An online soil survey for California, Arizona, and Nevada

Digital soil survey products represent one of the largest and most comprehensive inventories of soils information currently available. The complex structure of these databases, intensive use of codes and scientific jargon make it difficult for non-specialists to utilize digital soil survey resources. A project was initiated to construct a web-based interface to digital soil survey products (STATSGO and SSURGO) for California, Arizona, and Nevada that would be accessible to the general public. A collection of mature, open source applications (including Mapserver, PostGIS and Apache Web Server) were used as a framework to support data storage, querying, map composition, data presentation, and contextual links to related materials. Application logic was written in the PHP language to “glue” together the many components of an online soil survey. A comprehensive website ( http://casoilresource.lawr.ucdavis.edu/map) was created to facilitate access to digital soil survey databases through several interfaces including: interactive map, Google Earth and HTTP-based application programming interface (API). Each soil polygon is linked to a map unit summary page, which includes links to soil component summary pages. The most commonly used soil properties, land interpretations and ratings are presented. Graphical and tabular summaries of soil profile information are dynamically created, and aid with rapid assessment of key soil properties. Quick links to official series descriptions (OSD) and other such information are presented. All terminology is linked back to the USDA-NRCS Soil Survey Handbook which contains extended definitions. The Google Earth interface to Soil-Web can be used to explore soils information in three dimensions. A flexible web API was implemented to allow advanced users of soils information to access our website via simple web page requests. Soil-Web has been successfully used in soil science curriculum, outreach activities, and current research projects. Although not the only online soil survey application, Soil-Web remains the simplest and most rapid means of accessing soils information for California, Arizona, and Nevada.

Apparatus and method for providing server state and attribute management for voice enabled web applications

A unified web-based voice messaging system provides voice application control between a web browser and an application server via an hypertext transport protocol (HTTP) connection on an Internet Protocol (IP) network. The application server generates and maintains a server-side data record, also referred to as a “brownie”, that includes application state information and user attribute information for an identified user session with the web browser. The application server, in response to receiving a new web page request from the browser, initiates a web application instance to begin a transient application session with the browser. The brownie also includes a session identifier that uniquely identifies the session with the user of the application session. The application server stores the brownie in a memory resident within the server side of the network, and sends to the browser the session identifier and the corresponding web page requested by the web browser.

온톨로지를 이용한 의미 기반 정보 채움 시스템

테이블 형태로 이루어진 이력서 양식이나 인터넷 회원 가입에서 개인의 신상 정보를 매번 입력하는 일은 매우 반복적이고 번거로운 일이다. 개인의 신상 정보를 컴퓨터에 저장하고 있다가 인터넷 회원 가입 페이지에 자동으로 채워 주는 몇 개의 시스템이 나와 있으나 필드와 필드 값이 잘못 매치되는 등 정확도가 떨어지는 면이 있다. 본 연구는 컴퓨터에 개인의 신상정보를 저장하고 있다가 개인 데이터 온톨로지를 이용하여 회원가입 페이지(목표 페이지)에서 요구하는 사용자의 정보를 추론하고 자동으로 채워주는 시스템을 제안하였다. 추론의 과정에서 먼저 목표 페이지를 분석하여 요구하는 필드명을 추출하고, 유사어 온톨로지를 이용하여 요구 필드명을 표준 필드명으로 변환한다. 표준 필드명으로 변환된 요구 필드는 온톨로지 매치 메이킹을 이용하여 개인 데이터 온톨로지 상의 적절한 레벨을 찾아서 최종적인 필드값을 생성한다. 본 시스템은 목표 페이지와 유사한 필드를 가져올 뿐만 아니라 온톨로지 계층 상에 해당되는 필드를 추론하여 정확한 필드값을 가져오게 된다. 몇 개의 회원 가입 페이지를 대상으로 실험한 결과 본 시스템이 기존의 시스템에 비해 정확도에서 우수함을 보였다. 본 시스템은 이력서 양식 등 반복적으로 동일한 정보를 채우는 경우에도 쉽게 적용 가능하다. It is very iterative and complicated work to enter the personal information every time one fills the form-based resume or one joins the new membership page on the internet. Although there are some systems that have the personal information on the computer and fill the membership page automatically, their accuracies are not often satisfactory in that the fields and their values do not match exactly. The research proposes and implements a system that has user's information on the computer and reasons and fills the information automatically that a membership web page(target page) requests using the personal information ontology. During the reasoning process, the target page is analyzed to extract the requested fields. Then the requested field names are converted to the standard field names using synonym ontology. The converted requested fields find the appropriate level in the personal information ontology using ontology match making to generate the final field value. The system not only finds the similar fields but also generates the exact field values by reasoning on the information ontology hierarchy. By experimenting with several membership pages on the web, the system showed higher accuracy over the existing systems. The system can be easily applicable to the cases where one iteratively fills the same information such as resume form.

Mining user access patterns with traversal constraint for predicting web page requests

The recent increase in HyperText Transfer Protocol (HTTP) traffic on the World Wide Web (WWW) has generated an enormous amount of log records on Web server databases. Applying Web mining techniques on these server log records can discover potentially useful patterns and reveal user access behaviors on the Web site. In this paper, we propose a new approach for mining user access patterns for predicting Web page requests, which consists of two steps. First, the Minimum Reaching Distance (MRD) algorithm is applied to find the distances between the Web pages. Second, the association rule mining technique is applied to form a set of predictive rules, and the MRD information is used to prune the results from the association rule mining process. Experimental results from a real Web data set show that our approach improved the performance over the existing Markov-model approach in precision, recall, and the reduction of user browsing time.

Academic Data Collection in Electronic Environments: Defining Acceptable Use of Internet Resources

Academic researchers access commercial web sites to collect research data. This research practice is likely to increase. Is this appropriate? Is this legal? Such commercial web sites are maintained to achieve business objectives; research access uses site resources for other purposes. Web site administrators may, therefore, deem academic data collection inappropriate. Is there a process to make research access more open and acceptable to web site owners and administrators? These are significant issues. This article clarifies the problems and suggests possible approaches to handle the issues with sensitivity and openness. Research access to commercial web sites may be manual (using a standard web browser) or automated (using automated data collection agents). These approaches have different effects on web sites. Researchers using manual access tend to make a limited number of page requests because manual access is costly to perform. Researchers using automated access methods can request large numbers of pages at a low cost. Therefore, web site administrators tend to view manual access and automated access very differently. Because of the number of accesses and the nonbusiness purpose, automated research requests for data are sometimes blocked by site administration using a variety of means (both technological and legal). This paper details the pertinent legal issues including trespass, copyright violation, and breech of contract. It also explains the nature of express and implied consent by site administration for research access. Based on the issues presented, guidelines for researchers are proposed to reduce objections to research activities, to facilitate communication with web site administration, and to achieve express or implied consent. These include notification to web site administration of intended automated research activity, description of the research project posted as a web page, and clear identification of automated requests for web pages. In order to encourage good research practices with respect to automated data collection, suggestions are made with respect to disclosing methods used in research papers and for self regulation by academic associations.

Least-recently-used caching with dependent requests

We investigate a widely popular least-recently-used (LRU) cache replacement algorithm with semi-Markov modulated requests. Semi-Markov processes provide the flexibility for modeling strong statistical correlation, including the widely reported long-range dependence in the World Wide Web page request patterns. When the frequency of requesting a page n is equal to the generalized Zipf's law c / n α , α > 1 , our main result shows that the cache fault probability is asymptotically, for large cache sizes, the same as in the corresponding LRU system with i.i.d. requests. The result is asymptotically explicit and appears to be the first computationally tractable average-case analysis of LRU caching with statistically dependent request sequences. The surprising insensitivity of LRU caching performance demonstrates its robustness to changes in document popularity. Furthermore, we show that the derived asymptotic result and simulation experiments are in excellent agreement, even for relatively small cache sizes.

Faster Web page allocation with neural networks

To maintain quality of service, some heavily trafficked Web sites use multiple servers, which share information through a shared file system or data space. The Andrews file system (AFS) and distributed file system (DFS), for example, can facilitate this sharing. In other sites, each server might have its own independent file system. Although scheduling algorithms for traditional distributed systems do not address the special needs of Web server clusters well, a significant evolution in the computational approach to artificial intelligence and cognitive engineering shows promise for Web request scheduling. Not only is this transformation - from discrete symbolic reasoning to massively parallel and connectionist neural modeling - of compelling scientific interest, but also of considerable practical value. Our novel application of connectionist neural modeling to map Web page requests to Web server caches maximizes hit ratio while load balancing among caches. In particular, we have developed a new learning algorithm for fast Web page allocation on a server using the self-organizing properties of the neural network (NN).

Evaluations of architectural designs and implementation for database-driven web sites

Response time is a key differentiation point among electronic commerce (e-commerce) applications. For many e-commerce applications, Web pages are created dynamically based on the current business state, stored in database systems. The architecture of database-driven e-commerce Web sites are more complex than that of typical Web sites. It requires integration of Web servers, application servers, and back-end database systems as well as synchronization of multiple databases if caches are used for acceleration of content delivery. In this paper, we analyze the factors that impact the performance and scalability of a database-driven Web site. We experimentally test (1) the performance metrics of database update, query, and synchronization; (2) trigger overhead; and (3) application server (AS) and database connection overhead and constraints. We describe several architectural design approaches for database-driven Web sites and present experimental results on their performance under various conditions, including varying request rates, update-to-request ratio, cache hit ratio, number of regional AS/data cache suites deployed, and database size. We also discuss how to handle Web page requests that involve SSL, cookies, and fragment pages (i.e. by frame or JSP).

Dynamic Web pages: performance impact on Web servers

The World Wide Web has experienced phenomenal growth over the past few years, placing heavy load on Web servers. Today’s Web servers also process an increasing number of requests for dynamic pages, making server load even more critical. The performance of Web servers delivering static pages is well studied and well understood. However, there has been little analytic or empirical study of the performance of Web servers delivering dynamic pages. This paper focuses on experimentally measuring and analyzing the performance of the three dynamic Web page generation technologies: CGI, FastCGI and Servlets. In this paper, we present experimental results for Web server performance under CGI, Fast CGI and Servlets. Then, we develop a multivariate linear regression model and predict Web server performance under some typical dynamic requests. We find that CGI and FastCGI perform effectively the same under most low‐level benchmarks, while Servlets perform noticeably worse. Our regression model shows the same deficiency in Servlets’ performance under typical dynamic Web page requests.